Paper Info
Title
A Sparsity-Based Adaptive Channel Estimation Algorithm For Massive Mimo Wireless Powered Communication Networks
Abstract
Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2937183
IEEE ACCESS
Keywords
DocType
Volume
Massive MIMO, wireless powered communication networks, sparse channel estimation, sparsity-based adaptive matching pursuit, energy entropy-based order determination, staged adaptive variable step size
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Yuan Huang1127.67
Yigang He25519.50
Luqiang Shi302.03
Tongtong Cheng401.35
Yongbo Sui502.03
Wei He6102.24